LLM Summary Quality Prediction Under Cost Constraints
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Solution Overview
Problem
Conventional systems for generating summaries of large amounts of text using generative neural networks face inaccuracies, inefficiencies, and lack operational flexibility due to the challenges of understanding complex human language, computational resource usage, and inflexibility in model selection.
Innovation Solution
A digital document summary system that predicts summary quality scores and generation costs for multiple large language models using a quality prediction neural network and cost estimation algorithm, selecting the most accurate model for each text segment while adhering to a budget constraint.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single large language model is used to generate summaries, then the system is simple to operate, but the accuracy and quality of summaries vary widely and may not capture essential information
Solution Approach 1:
The patent segments the summarization task by dividing the document into multiple text segments and assigning different large language models to different segments based on their strengths. This allows each model to specialize in specific types of content, improving overall summary quality while managing complexity through structured division of labor
Solution Approach 2:
The system implements a multi-functional architecture that supports multiple large language models with different capabilities (e.g., GPT-3.5-Turbo for cost-effectiveness, GPT-4 for high accuracy, Claude for specific tasks). The framework can dynamically select and switch between models based on document type, segment importance, and quality requirements, providing universal adaptability across diverse summarization needs
2Measurement precision
If multiple large language models are invoked to improve summary accuracy, then the measurement precision improves, but the computational resource usage and generation costs increase
Solution Approach 1:
The system performs preliminary analysis by using a quality prediction neural network to evaluate which large language models will produce the highest quality summaries for specific text segments before actual summarization. This preliminary assessment allows the system to pre-select the most appropriate models, avoiding unnecessary invocation of multiple models and reducing computational waste
Solution Approach 2:
The system dynamically changes operational parameters by adjusting model selection, temperature settings, and generation parameters based on the specific characteristics of each text segment. This allows optimization of computational resource usage by using simpler models for routine segments and more powerful models only when necessary for complex or critical content
3Adaptability or versatility
If multiple large language models are used with different capabilities, then the adaptability and operational flexibility improve, but the device complexity increases
Solution Approach 1:
The patent introduces a quality prediction neural network as an intermediary component that mediates between the multiple large language models and the summarization task. This intermediary automatically evaluates and selects the most appropriate model for each text segment, simplifying the system architecture by centralizing the decision-making process and reducing the complexity of direct model management
4Measurement precision
If high-accuracy large language models are used for all text segments, then the summary quality is maximized, but the generation costs and time consumption increase due to diminishing returns
Solution Approach 1:
The system applies local quality optimization by matching different large language models to specific text segments based on their content characteristics, importance, and complexity. High-accuracy models like GPT-4 are applied only to critical or complex segments where they provide substantial value, while simpler models handle routine segments, optimizing the quality-cost ratio and avoiding diminishing returns from uniform high-level model deployment
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for predicting summary quality scores and determining summary generation costs of large language models to generate a digital document summary. In particular, in one or more embodiments, the disclosed systems extract one or more text segments from a digital document. Further, the disclosed systems generate, utilizing a quality prediction neural network, a predicted summary quality score for each of a plurality of large language models for the one or more text segments. Furthermore, the disclosed systems select a large language model from the plurality of large language models based on the predicted summary quality scores. Moreover, the disclosed systems generate, utilizing the selected large language model, a summary of the digital document.